Predicted USDA soil suborders at 250 m (probabilities)
Distribution of the USDA suborders based on machine learning predictions of great groups (https://doi.org/10.5281/zenodo.1476844) from global compilation of soil profiles. To learn more about soil suborders and great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - USDA. Proces...
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ftdatacite:10.5281/zenodo.2657408 2023-05-15T14:15:22+02:00 Predicted USDA soil suborders at 250 m (probabilities) Hengl, Tomislav Nauman, Travis 2019 https://dx.doi.org/10.5281/zenodo.2657408 https://zenodo.org/record/2657408 en eng Zenodo https://dx.doi.org/10.5281/zenodo.2657407 Open Access Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode cc-by-sa-4.0 info:eu-repo/semantics/openAccess CC-BY-SA LandGIS soil type USDA soil taxonomy dataset Dataset 2019 ftdatacite https://doi.org/10.5281/zenodo.2657408 https://doi.org/10.5281/zenodo.2657407 2021-11-05T12:55:41Z Distribution of the USDA suborders based on machine learning predictions of great groups (https://doi.org/10.5281/zenodo.1476844) from global compilation of soil profiles. To learn more about soil suborders and great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - USDA. Processing steps are described in detail here . Antartica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, suborder = variable: USDA suborder, usda.ustolls = determination method: USDA soil taxonomy class Ustolls, p = probability, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: soil surface, 1950..2017 = time reference: period 1950-2017, v0.1 = version number: 0.1, : {"references": ["Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagoti\u0107 A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748.", "Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0.", "USDA-NRCS, (2014). Illustrated Guide to Soil Taxonomy: Version 1.0, September 20014, U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center."]} Dataset antartic* DataCite Metadata Store (German National Library of Science and Technology) Gonzalez ENVELOPE(-58.250,-58.250,-63.917,-63.917) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
English |
topic |
LandGIS soil type USDA soil taxonomy |
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LandGIS soil type USDA soil taxonomy Hengl, Tomislav Nauman, Travis Predicted USDA soil suborders at 250 m (probabilities) |
topic_facet |
LandGIS soil type USDA soil taxonomy |
description |
Distribution of the USDA suborders based on machine learning predictions of great groups (https://doi.org/10.5281/zenodo.1476844) from global compilation of soil profiles. To learn more about soil suborders and great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - USDA. Processing steps are described in detail here . Antartica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: sol = theme: soil, suborder = variable: USDA suborder, usda.ustolls = determination method: USDA soil taxonomy class Ustolls, p = probability, 250m = spatial resolution / block support: 250 m, s0..0cm = vertical reference: soil surface, 1950..2017 = time reference: period 1950-2017, v0.1 = version number: 0.1, : {"references": ["Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagoti\u0107 A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748.", "Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0.", "USDA-NRCS, (2014). Illustrated Guide to Soil Taxonomy: Version 1.0, September 20014, U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center."]} |
format |
Dataset |
author |
Hengl, Tomislav Nauman, Travis |
author_facet |
Hengl, Tomislav Nauman, Travis |
author_sort |
Hengl, Tomislav |
title |
Predicted USDA soil suborders at 250 m (probabilities) |
title_short |
Predicted USDA soil suborders at 250 m (probabilities) |
title_full |
Predicted USDA soil suborders at 250 m (probabilities) |
title_fullStr |
Predicted USDA soil suborders at 250 m (probabilities) |
title_full_unstemmed |
Predicted USDA soil suborders at 250 m (probabilities) |
title_sort |
predicted usda soil suborders at 250 m (probabilities) |
publisher |
Zenodo |
publishDate |
2019 |
url |
https://dx.doi.org/10.5281/zenodo.2657408 https://zenodo.org/record/2657408 |
long_lat |
ENVELOPE(-58.250,-58.250,-63.917,-63.917) |
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Gonzalez |
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Gonzalez |
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antartic* |
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antartic* |
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https://dx.doi.org/10.5281/zenodo.2657407 |
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Open Access Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode cc-by-sa-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY-SA |
op_doi |
https://doi.org/10.5281/zenodo.2657408 https://doi.org/10.5281/zenodo.2657407 |
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